Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
This study explores the use of Federated Learning (FL) for tuberculosis (TB)
diagnosis using chest X-rays in low-resource settings across Africa. FL allows
hospitals to collaboratively train AI models without sharing raw patient data,
addressing privacy concerns and data scarcity that hinder traditional
centralized models. The research involved hospitals and research centers in
eight African countries. Most sites used local datasets, while Ghana and The
Gambia used public ones. The study compared locally trained models with a
federated model built across all institutions to evaluate FL's real-world
feasibility. Despite its promise, implementing FL in sub-Saharan Africa faces
challenges such as poor infrastructure, unreliable internet, limited digital
literacy, and weak AI regulations. Some institutions were also reluctant to
share model updates due to data control concerns. In conclusion, FL shows
strong potential for enabling AI-driven healthcare in underserved regions, but
broader adoption will require improvements in infrastructure, education, and
regulatory support.
